G-protein coupled receptors (GPCRs), are the largest superfamily of proteins in the human body. As ubiquitous transmembrane receptors, they modulate a vast number of physiological processes. They are the protein family most frequently targeted by drugs, >30% of all marketed therapeutics. Adrenergic receptors are a class of GPCRs that mediate the actions of the hormones adrenaline and noradrenaline. The B2-adrenergic receptor (B2AR) is responsible for the relaxation of vascular, uterine and airway smooth muscle. B1AR antagonists (beta-blockers) are widely used in cardiovascular therapy for the treatment of angina, arrhythmias, and hypertension. B2AR agonists (activators) are used to treat asthma, COPD and preterm labor. The difficulty of membrane protein x-ray crystallography has severely limited the application of computational structure-based drug discovery against this class of proteins. Recently, the determination of x-ray structures of the human B2AR has provided the first template for rational structure-based drug design. Virtual screening (also called rational drug design or molecular docking) is widely used in drug discovery to extract promising lead compounds from large chemical libraries. Two challenges faced by rational drug design are (1) the treatment of drug target flexibility, often crucial for ligand binding and (2) the efficient sampling of chemical space. We propose to use multi-conformational docking screens to address target flexibility. To generate multiple conformations, we will use two complimentary and distinct computational sampling methods: molecular dynamics, an accurate but computationally expensive method, and normal mode analysis, a coarse-grained but inexpensive method. In previous virtual screens against the B2AR rigid receptor structure, exclusively inverse-agonists (inhibitors) were found, presumably because of bias from the experimental inverse-agonist bound structure. We propose to understand the structural basis of activation through the binding modes of novel activating compounds found in our multi-conformational docking screens. New compounds will be tested experimentally for binding and efficiency. To address chemical space coverage, we will use fragment-based virtual screening. Fragment-based screening focuses on small compounds, smaller than canonical drug leads, with few functional groups. Fragment libraries provide a far greater coverage of chemical space because as molecular size decreases, the number of possible molecules that can be constructed decreases exponentially. As fragment screens uncover new chemical scaffolds, we will look again at top-scoring hits from previous docking screens using larger lead-like molecules to identify additional compounds for further testing.